Online learning of windmill time series using Long Short-term Cognitive Networks

Autor: Alejandro Morales-Hernández, Gonzalo Nápoles, Agnieszka Jastrzebska, Yamisleydi Salgueiro, Koen Vanhoof
Přispěvatelé: Cognitive Science & AI, MORALES HERNANDEZ, Alejandro, Salgueiro, Yamisleydi, NAPOLES RUIZ, Gonzalo, VANHOOF, Koen, Jastrzebska, Agnieszka
Rok vydání: 2021
Předmět:
Zdroj: Expert Systems with Applications, 205:117721, 1-9. Elsevier Limited
Joint International Scientific Conferences on AI and Machine Learning BNAIC/BeNeLearn 2022
Tilburg University-PURE
ISSN: 0957-4174
DOI: 10.48550/arxiv.2107.00425
Popis: Forecasting windmill time series is often the basis of other processes such as anomaly detection, health monitoring, or maintenance scheduling. The amount of data generated by windmill farms makes online learning the most viable strategy to follow. Such settings require retraining the model each time a new batch of data is available. However, updating the model with new information is often very expensive when using traditional Recurrent Neural Networks (RNNs). In this paper, we use Long Short-term Cognitive Networks (LSTCNs) to forecast windmill time series in online settings. These recently introduced neural systems consist of chained Short-term Cognitive Network blocks, each processing a temporal data chunk. The learning algorithm of these blocks is based on a very fast, deterministic learning rule that makes LSTCNs suitable for online learning tasks. The numerical simulations using a case study involving four windmills showed that our approach reported the lowest forecasting errors with respect to a simple RNN, a Long Short-term Memory, a Gated Recurrent Unit, and a Hidden Markov Model. What is perhaps more important is that the LSTCN approach is significantly faster than these state-of-the-art models.
Databáze: OpenAIRE
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